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Record W3046762186

Habitat use of young-of-year Arctic Grayling (Thymallus arcticus) in Barrenland streams of central Nunavut, Canada

2020· dissertation· en· W3046762186 on OpenAlexfundaboutno aff
Jared R. Ellenor

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGraylingSTREAMSArcticHabitatGeographyFisheryEcologyEnvironmental scienceBiologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Arctic Grayling, a species within the family Salmonidae that is valued by sport fishers and Indigenous communities, is distributed throughout a diversity of northern landscapes. While Arctic Grayling are known to be sensitive to perturbations in habitat and water quality, our understanding of constraints on their distribution is incomplete, particularly in the vast subarctic Barrenlands region. Understanding the habitat requirements and distribution of Barrenland populations of Arctic Grayling is necessary to develop effective conservation policies, avoid or mitigate potential impacts of mining and other development, and evaluate population distribution trends over time. Barrenland populations of Arctic Grayling rely on seasonally connected networks of lakes and streams to migrate, spawn, and rear. Knowledge of stream conditions and characteristics that are suitable for rearing young-of-year Arctic Grayling is critical for understanding and predicting variability in recruitment, and thus to ensuring the continued persistence of Barrenland populations. In summer 2019, visual surveys assessing the presence/absence of young-of-year Arctic Grayling were conducted at 49 streams in the Barrenlands region near Baker Lake, Nunavut. Occupancy modeling was used to relate a comprehensive suite of stream habitat (e.g., depth, velocity, water temperature) and landscape (e.g., land cover, contributing upstream lake area) variables to the presence/absence of young-of-year Arctic Grayling. Quantification of detection efficiency, and variables that affect detection efficiency, allowed for improved inferences on species-habitat relationships. While detection efficiency was negatively influenced by water depth and water velocity, the best predictors of young-of-year grayling occupancy were the total area of contributing upstream lakes and the landcover (upland/lowland) of the stream basin. These results suggest that the position of streams within Barrenland landscapes is related to reliability of stream connectivity, and thus suitability for young-of-year. Both explanatory variables are important in promoting hydrologic connectivity throughout the summer rearing period and facilitating the migration of young-of-year to overwintering lakes prior to freeze up. Contributing upstream lake area and land classification data may be obtained remotely, which allows for preliminary predictions of stream suitability to be conducted with minimal financial and logistic effort, and more spatially focused field operations. The occupancy model developed here can be used as a valuable predictive tool for Arctic Grayling young-of-year stream use in the Barrenlands, and will facilitate regulators, scientists, resource managers, and industry in developing more effective conservation and mitigation plans for fish and fish habitat in areas of resource development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.166
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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